Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review
department or school: Medicine, Faculty of
department or school: Medicine, Department of
data provider: DSpace
identifier citation: Bioengineering 11 (5): 489 (2024)
creator: Rabkin, Simon
issued: 2024-06-07T17:20:09Z
issued: 2024-05-15
description: Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy, electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88421
note: Citation: Rabkin, S.W. Searching forthe Best Machine Learning Algorithmfor the Detection of Left VentricularHypertrophy from the ECG: AReview. Bioengineering 2024, 11, 489.https://doi.org/10.3390/bioengineering11050489Academic Editor: Giuseppe BaselliReceived: 2 April 2024Revised: 29 April 2024Accepted: 2 May 2024Published: 15 May 2024Copyright: © 2024 by the author.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).bioengineeringReviewSearching for the Best Machine Learning Algorithm for theDetection of Left Ventricular Hypertrophy from the ECG:A ReviewSimon W RabkinDepartment of Medicine, Division of Cardiology, University of British Columbia, 9th Floor 2775 Laurel St.,Vancouver, BC V5Z 1M9, Canada; simon.rabkin@ubc.ca; Tel.: +1-(604)-875-5847; Fax: +1-(604)-875-5849Abstract: Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardio-vascular events. Objectives: The objectives of this study were to conduct a systematic review ofmachine learning (ML) algorithms for the identification of LVH and compare them with respect tothe classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria forLVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy,electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched.Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized atleast one ML approach. ML approaches encompassed support vector machines, logistic regression,Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural net-works, convolutional neural networks, deep neural networks and a back-propagation neural network.Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison wi…
has type: Article
is shown at: 10.14288/1.0443919
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/bioengineering11050489
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
subject: left ventricular hypertrophy
subject: electrocardiogram
subject: machine learning
subject: artificial intelligence
type: Text
identifier URI: http://hdl.handle.net/2429/88421